1. Technical Field of the Invention
The present invention relates in general to single antenna interference rejection (SAIR) in digital time-division multiple access communications systems and, more particularly, to SAIR in communications systems operating according to Global System of Mobile communications (GSM).
2. History of Related Art
Receiver performance in wireless digital time-division multiple access (TDMA) communication systems such as, for example, those operating according to GSM, Enhanced Data GSM Evolution (EDGE), and Digital Advanced Mobile Service (DAMPS), is often interference-limited. Interference might come from, for example, other users. Users operating on identical carrier frequencies in neighboring cells might create co-channel interference (CCI), while users operating on adjacent carrier frequencies might create adjacent-channel interference (ACI).
Network capacity of the communication systems is limited by cellular frequency planning, which has to comply with the CCI and ACI performance of the receivers. Thus, any substantial improvement to receiver CCI or ACI performance can significantly increase the network capacity. SAIR is an approach used to increase the network capacity.
SAIR utilizes the single-dimensional nature in the complex domain of interference in, for example, a GSM system. Real and imaginary samples of a complex received signal are treated as if the real and imaginary samples were from different propagation channels. Spatio-temporal diversity is exploited to suppress interference.
FIG. 1 is a functional block diagram that illustrates a current SAIR process. In FIG. 1,  indicates a dependency relation. Variables shown in FIG. 1 are as follows:                r received signal        r′ updated received signal        t training sequence        h{n+m} n+m tap channel estimate        A{m+1} interference model        p synchronization position        s symbol estimate        
A SAIR process 100 begins with burst synchronization of the received signal r at a synchronization block 102. At a Generalized Least Square (GLS) channel Spatio-Temporal Whitening (STW) estimation block 104, a joint estimation of an m-th order Vectorized Auto-Recursive (VAR) model of the interference A{m+1} and the (n+m)-th order channel estimate h{n+m} is performed. The interference model A{m+1} is output from the GLS channel STW estimation block 104 to a STW filter block 106. The received signal r is also input to the STW filter block 106. The received signal r is filtered by the STW filter block 106 with coefficients in a matrix polynomial to yield an updated received signal r′.
The equalizer 108 also receives the interference model A{m+1} from the GLS channel STW estimation block 104. The equalizer 108 estimates data symbols of the transmission and yields the symbol estimate s.
In a GSM system, for example, the order of the interference model must be sufficiently low (e.g., m<3) for identifiability and complexity reasons. There are at least two drawbacks of the process 100. First, a span of the composite channel (i.e., the channel seen from the equalizer) for the received signal r is extended from n symbols to (n+m) symbols by the process 100. For a Decision Feedback Sequence Estimation (DFSE) equalizer, the channel extension is not a critical drawback, since a feedback span extension does not impose any prohibitive complexity increase. However, for a Maximum Likelihood Sequence Estimator (MLSE) equalizer, a complexity increase due to the process 100 is exponential. If, as in many common platforms, a Viterbi processor for the MLSE equalizer is pre-built with dedicated hardware, the extension makes implementation of SAIR as shown in the process 100 very difficult, if not impossible.
Second, a joint GLS estimation of the interference model A{m+1} and the n+m tap channel estimate h{n+m} is both computationally expensive and often inaccurate. The inaccuracy impairs receiver performance under many channel conditions, such as, for example, when no strong interference is present or with high-level background noise. The background noise can be caused not only by physical disturbances, such as thermal noise, but also by non-ideal implementation factors, such as, for example, quantization noise. In addition, numerical properties of the joint estimation can be also critically ill-conditioned, which can make a fixed-point implementation of the algorithm very demanding. In GLS estimation, inversion of a fairly large matrix (e.g., up to 13×13) must be solved. Under realistic channel conditions, the matrix is close to singular, which makes fixed-point computations unstable due to rounding or truncation errors.